Work

ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets

Year: 2018

Type: conference-paper

Abstract: This paper describes the ALANIS system that participated in Task 3 of SemEval-2018.We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony.The system uses a logistic regression model in subtask A and a voted classifier in subtask B, both of which rely on manually developed features to identify ironic tweets.ALANIS placed 34th of 43 systems in subtask A and 26th of 31 systems in subtask B. *Authors are ordered alphabetically by their first name.respectively on the test set.The F1-scores are .469and .276 on the test set.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s18-1082

Authors: Kevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang

Institution: University of Minnesota, Duluth

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: University of Minnesota

OpenAlex ID: W2807079909